computes the (sample) partial autocorrelation function for the time series x. The output vector starts with the partial autocorrelation coefficient phi_1 at lag 1. The next entries are phi_2 ... phi_k.
Calculates the parzen window. Parzen windows classification is a technique for nonparametric density estimation, which can also be used for classification. In the Parzen window (Parzen, 1961), for each frequency, the weights for the weighted moving average of the periodogram values are computed as:
plmhetmean estimates the parameter part in partially linear heteroscedastic models, in which the variance is an unknown function of the mean. We use the replication technique to estimate the variance functions.
shows a coxcomb plot, a special pie chart, where the frequency of an observed attribute is proportional to the area of the corresponding segment and the angles of the segments are all equal. Consequently, the frequency of the attribute is proportional to the square of the radius of the segment.
Plots impulse response functions (ir) of a K-dimensional VAR(p) optionally with up to four different confidence intervals or confidence limits. Different methods for computing confidence intervals for impulse responses are thus graphically comparable. Optionally, the appearance of the graphical out
auxiliary quantlet for grcarttree Generates vectors of characters to be displayed. For the non-terminal nodes prints the splitting variable and the splitting point. The information is printed in the following form: "X1<=5.2", that is, variables are denoted with X1,...,Xp. For the terminal nodes the
exchanges the category labels of a vector into other ones. Here the old category labels as well as the targed category labels have to be specified. Don't specify categories which should remain unchanged